Research in Prevention Laboratory (MacKinnon)

David MacKinnon, Ph.D. is the faculty director of RiPL and a Regents and Foundation Professor in the Department of Psychology at Arizona State University. He received his B.A. from Harvard University and Ph.D. in Measurement and Psychometrics from UCLA. His current research interests are in statistical methods, particularly as applied in health promotion and disease prevention research. He also conducts research on the role of social influence and cognitive factors in health behavior.

Jenn-Yun Tein, Ph.D. is a research professor and the Director of the Research Methodology Core at the ASU REACH Institute, with the Department of Psychology. She received her doctorate in quantitative psychology from the Ohio State University.  Her research focuses on program evaluations and various statistical and methodological applications and issues, including measurement, longitudinal modeling, mixture model analysis, multilevel analysis, and survival analysis. She is specifically interested in research and application of mediation and moderation models in prevention research to investigate how an intervention achieves the effects and on which groups or under what conditions the intervention has effects. She also has extensive research experience with children and families of minority populations.

Current Team Members

Sophia Lamp, M.A. is a fifth-year graduate student in the Quantitative Research Methods Ph.D. program at Arizona State University. She received her B.S. in Psychological Science from the University of Mary Washington and M.A. in Quantitative Research Methods from ASU. Her research interests include mediation analysis, collider effects and selection bias in psychological studies, power and sample size requirements for complex statistical models, causal inference, and prevention research.   

Diana Alvarez Bartolo, M.A. is a Ph.D. candidate in the Quantitative Research Methods program at Arizona State University (ASU) and has collaborated in the Research in Prevention Laboratory under the direction of Dr. David MacKinnon since 2019. She received her B.S. in Psychology from the National Autonomous University of Mexico, known by its acronym in Spanish (UNAM), and her M.A. in Quantitative Research Methods from ASU. She was a Fulbright García-Robles grantee from 2018 to 2022. Currently, her research interests include causal inference, mediation analysis, sensitivity analyses, and prevention research.

Alexis R. Georgeson, Ph.D. is a Ruth L. Kirschstein NRSA Postdoctoral Fellow at Arizona State University. She earned her Ph.D. in Quantitative Psychology from the University of North Carolina at Chapel Hill and her M.A. in Experimental Psychology from Simon Fraser University. Her methodological research interests include measurement, mediation, and longitudinal models. She is interested in applications of advanced quantitative methods to substance use and interventions. 

Augusto-Jovito ‘AJ’ Palo is an undergraduate research assistant in the lab. He is interested in using an evolutionary perspective to better understand moral judgment and motivated reasoning. After graduating, he plans to pursue a doctoral degree in social psychology.

Recent Alumni

Heather Smyth, Ph.D. is now a Research Associate in the Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus. Dr. David MacKinnon was Heather’s advisor. She is interested in statistical models for prevention research in public health and education. Her research focuses on individual differences in modern mediation methods, causal inference of mediation models, and categorical data analysis.

Jennifer Somers, Ph.D. is now an Assistant Professor in clinical psychology at Auburn University. She earned her Ph.D. and  M.A. in Clinical Psychology from Arizona State University and her B.A. in Psychology and English from Pomona College. Her research focuses on how early close relationships support lifespan health within low-resource populations, including (a) individual differences in how early parent-child relationships influence lifespan health and (b) transactional processes within close relationships that influence wellbeing. At RiPL, she worked on methods for mediation in single-case designs and for examining individual differences in response to environmental influences.

Kiana Guarino received her B.S. in Psychology (2023) from Arizona State University. Kiana is now a graduate student in quantitative methods at Arizona State University. Her advisor is Dr. Samantha Anderson.

Felix Muniz received his B.S. in Psychology (2015) and Mathematics (2017) from Arizona State University. He is an Interdisciplinary Enrichment Fellow and an American Indian Education Fund scholar. He is now working on his dissertation on psychometrics supervised by Dr. Michael Edwards. His research interests include psychometric and measurement issues, prevention science methods, psychological and mental health outcomes in Indigenous populations, and the dissemination of quantitative methods in Indigenous studies. In particular, he has conducted research on suppression effects.

Matthew Valente, Ph.D. earned his B.S. in psychology with a minor in statistics from the University of North Florida. He earned his M.A. and Ph.D. in quantitative psychology from Arizona State University working with Dr. David MacKinnon. His research interests include longitudinal mediation models, potential outcomes framework for causal inference in mediation models, and potential outcomes framework for causal inference in longitudinal models. His research interests also include prevention science methodology.  Matt is now an Assistant Professor in the biostatistics department at the University of South Florida.

Oscar Gonzalez, Ph.D. earned his B.A. in Psychology and a minor in European Studies from the University of Notre Dame. He also earned his M.A. and Ph.D. in Quantitative Psychology from Arizona State University under the direction of Dr. David MacKinnon.  His research interests include psychometric and measurement issues in statistical mediation, item selection for short scale development, and data mining/big data techniques for the social sciences. Oscar also has interned at the Educational Testing Service (ETS) working on the NAEP (the Nation's Report Card) agenda for assessment innovations in early science education. He was also a National Science Foundation graduate research fellow at Arizona State. Oscar is now an Assistant Professor in the psychology department at the University of North Carolina, Chapel Hill.

Gina Mazza, Ph.D. earned a Ph.D. in Psychology, Quantitative Research Methods at Arizona State University under the direction of Drs. Kevin Grimm and Stephen West.  She also received an M.A. in Psychology, Quantitative Research Methods under the direction of Dr. Craig Enders and a B.S. in Psychology with a minor in Mathematics.  Her research focuses on the development and advancement of methods for addressing two pervasive issues in medical, pharmacological, and psychological research—missing data and treatment non-adherence. Gina is now a statistician at the Mayo Clinic in Scottsdale Arizona.

Jessica Canning is now a Ph.D. student at the University of Washington in Seattle, WA. She earned her B.S. in Psychology (Psychological Sciences) and was an undergraduate research assistant in the Research in Prevention Lab (RiPL) at Arizona State University. Jessica is interested in researching how variability of state-level characteristics, such as impulsivity and self-regulation, contribute to the development of alcohol and substance use disorders.

Amanda Baraldi, Ph.D. received her B.S. in mathematics from UMass Amherst, M.A. in Clinical Psychology from Columbia University, and Ph.D. in Quantitative Psychology from Arizona State University. She formerly worked for the Nathan Kline Institute. Her current research interests include missing data analyses, methods for assessing mediation, longitudinal growth modeling, and health and prevention research. Amanda is now an Associate Professor at Oklahoma State University.

Hanjoe Kim, Ph.D. received his M.A. from SungKyunKwan University in Seoul, South Korea, and his Ph.D. in Quantitative Psychology from Arizona State University. His current research interests are in statistical methods such as survival analysis, mediation analysis, multilevel analysis, and measurement invariance. He is also interested in applying these methods to prevention science. Hanjoe is now an Assistant Professor position in the psychology department at Yonsei University in Seoul, South Korea.

Milica Miočević, Ph.D. earned her Ph.D. in Quantitative Psychology from Arizona State University. Drs. Roy Levy and David MacKinnon were co-chairs of her dissertation committee. Her research interests include Bayesian mediation, Bayesian SEM, statistical methods for improving power in small samples, data synthesis, and causal inference in mediation models. Milica is now an Assistant Professor at McGill University.

Holly O’Rourke, Ph.D. earned her B.S., M.A., and Ph.D. in Quantitative Psychology from Arizona State University under the direction of Dr. David MacKinnon. Her current research interests are in power and significance testing methods for mediation models, causal inference, effect sizes for mediation, and health and prevention research, with specific attention to methods in addiction and alcohol treatment research. Holly is now an Assistant Professor in the Family and Human Development Department at ASU.

Ingrid Carlson Wurpts, Ph.D. received her B.A. in Mathematics and Psychology from Northwestern College and M.A. & Ph.D. in Quantitative Psychology from Arizona State University. She is interested in latent class analysis, person-oriented and idiographic mediation models, as well as the psychology of eating and eating prevention methodology. Ingrid is now a Data Scientist for Banner Health.

Supplementary Materials

•  Benchmark Validation of Statistical Models: An application of mediation analysis of imagery and memory
•  A Tutorial in Bayesian Potential Outcomes Mediation Analysis
•  Comparing Models of Change to Estimate the Mediated Effect in the Pretest-Posttest Control Group Design
•  Technical report for memory studies with data 2014

MEMOs from Shield Researchers

• MULTILEVEL SHIELD ANALYSES AND EFFECT SIZE CALCULATION  4/04/14: Shield 6 Month Analysis Addendum
• 24 MONTH PAPER SCALE AND PHYSIOLOGICAL INDEX INFORMATION  2/19/16: Shield 24 Month Constructs Addendum

Select Publications (since 2020)

2024

• Lamp, S. J., & MacKinnon, D. P. (in press). Correcting for collider effects and sample selection bias in psychological research. Accepted for publication at Psychological Methods.
• Alvarez-Bartolo, D., & MacKinnon, D. P. (2024). Alternative approaches to estimate causal mediated effects in the single-mediator model. Multivariate Behavioral Research. Advance online publication. https://doi.org/10.1080/00273171.2024.2310395
• Lamp, S. J., & MacKinnon, D. P. (2024). Correcting regression coefficients for collider bias in psychological research. Multivariate Behavioral Research. Advance online publication. https://doi.org/10.1080/00273171.2024.2310418
• Brincks, A. M., MacKinnon, D. P., Gustafson, D. H., & McKay, J. R. (in press). Using causal mediation to examine self-efficacy as a mechanism through which continuing care interventions reduce alcohol use. Psychology of Addictive Behaviors. 

2023

• Georgeson, A. R., Alvarez-Bartolo, D., & MacKinnon, D. P. (2023). A Sensitivity Analysis for Temporal Bias in Cross-Sectional Mediation. Psychological Methods. Advance online publication. https://doi.org/10.1037/met0000628
• Lamp, S. J., Alvarez-Bartolo, D., MacKinnon, D. P., & Luecken, L. J. (2023). Methods for studying mediating mechanisms in developmental and intervention studies of child maltreatment. In C. E. Shenk (Ed.), Innovative Methods in Child Maltreatment Research and Practice. Springer Nature.
• Moon, J. W., Cohen, A. B., Laurin, K., & MacKinnon, D. P. (2023). Is religion special? Perspectives on Psychological Science, 18(2), 340-357. https://doi.org/10.1177/17456916221100485
• Blake, A. J., MacKinnon, D. P., Waddell, J. T., & Chassin, L. (2023). Parent-child separation and intergenerational transmission of substance use and disorder: Testing across three generations. Development and Psychopathology, 36(1), 28-39. https://doi.org/10.1017/S0954579422000876

2022

• Wurpts, I. C., Miočević, M., & MacKinnon, D. P. (2022). Sequential Bayesian data synthesis for mediation and regression analysis. Prevention Science, 23(3), 378-389.
•  MacKinnon, D. P., Smyth, H. L., Somers, J., Ho, E., Norget, J., & Miočević, M. (2022). A randomization permutation mediation test for single subject mediation. Evaluation & the Health Professions, 45(1), 54-65. https://doi.org/10.1177/01632787211070811
• Waddell, J. T., Corbin, W. R., MacKinnon, D. M., Leeman, R. H., DeMartini, K. S., Fucito, L., Kranzler, H., & O’Malley, S. S. (2022). Within-and between-person effects of Naltrexone on subjective response to alcohol and craving: A daily diary investigation. Alcoholism: Clinical and Experimental Research, 46(3), 477-491.
• Kruger, E., Tofighi, D., Hsiao, Y.-Y., MacKinnon, D. P., Van Horn, M. L., & Witikiewitz, K. (2022). Teacher’s Corner: An R Shiny app for sensitivity analysis for latent growth mediation. Structural Equation Modeling: A Multidisciplinary Journal, 29(6), 944-952.
• Scherer, E. A., Kim, S. J., Metcalf, S. A., Sweeney, M. A., Wu, J., Xie, H., Mazza, G. L., Valente, M. J., MacKinnon, D. P., & Marsch, L. A. (2022). Momentary self-regulation: Scale development and preliminary validation. JMIR Mental Health, 9(5), Article e35273. https://doi.org/10.2196/35273
• Scherer, D., Metcalf, S. A., Whicker, C. L., Bartels, S. M., Grabinski, M., Kim, S. J., Sweeney, M. A., Lemley, S. M., Lavoie, H., Xie, H., Bissett, P. G., Dallery, J., Kiernan, M., Lowe, M. R., Onken, L., Prochaska, J., Stoeckel, L., Poldrack, R. A., MacKinnon, D. P., & Marsch, L. A. (2022). Momentary influences on self-regulation in two populations with health risk behaviors: Adults who smoke and adults who are overweight and have binge-eating disorder. Frontiers in Digital Health, 4, Article 798895. https://doi.org/10.3389/fdgth.2022.798895
• McNeish, D., & MacKinnon, D. P. (2022). Intensive longitudinal mediation in Mplus. Psychological Methods. Advance online publication. https://doi.org/10.1037/met0000536
• Vo, T. T., Cashin, A. G., Superchi, C., Tu, P. H. T., Nguyen, T. B., Boutron, I., MacKinnon, D. P., VanderWeele, T., Lee, H., & Vansteelandt, S. (2022). Quality assessment practice in systematic reviews of mediation studies: Results from an overview of systematic reviews. Journal of Clinical Epidemiology, 143, 137-148. https://doi.org/10.1016/j.jclinepi.2021.12.013

2021

• MacKinnon, D. P., & Lamp, S. J. (2021). A unification of mediator, confounder, and collider effects. Prevention Science, 22(8), 1185-1193.  
• Lee, H., Cashin, A. G, Lamb, S. E, Hopewell, S., Vansteelandt, S., VanderWeele, T. J., MacKinnon, D. P., Collins, G., Golub, R., McAuley, J., & the AGReMA Group. (2021). A guideline for reporting mediation analyses of randomized trials and observational studies: The AGReMA Statement. Journal of the American Medical Association, 326(11), 1045-1056. https://doi.org/10.1001/jama.2021.14075
• Van Liew, C., Monaghan, A., Dibble, L. E., Foreman, K. B., MacKinnon, D. P., & Peterson, D. S. (2021). Perturbation practice in multiple sclerosis: assessing generalization from support surface translations to tether-release tasks. Multiple Sclerosis and Related Disorders, 56, Article 103218. https://doi.org/10.1016/j.msard.2021.103218
• Rijnhart, J. J., Lamp, S. J., Valente, M. J., MacKinnon, D. P., Twisk, J. W., & Heymans, M. W. (2021). Mediation analysis methods used in observational research: a scoping review and recommendations. BMC Medical Research Methodology, 21, Article 226. https://doi.org/10.1186/s12874-021-01426-3 
• Smyth, H. L, Pitpitan, E. V., MacKinnon, D. P., & Booth, R. E. (2021). Assessing potential outcomes mediation in HIV interventions. AIDS and Behavior, 25(8), 2441-2454. https://doi.org/10.1007/s10461-021-03207-x
• O’Rourke, H. P., Fine, K. L., Grimm, K. J., & MacKinnon, D. P. (2021). The importance of time metric precision when implementing bivariate latent change score models. Multivariate Behavioral Research, 57(4), 561-580. https://doi.org/10.1080/00273171.2021.1874261
• McNeish, D., MacKinnon, D. P., Marsch, L. A., & Poldrack, R. A. (2021). Measurement in intensive longitudinal data. Structural Equation Modeling, 28(5), 807-822. https://doi.org/10.1080/10705511.2021.1915788
• Pipitan, E. V., MacKinnon, D. P., Eaton, L. A., Smith, L. R., Wagman, J., & Patterson, T. L. (2021). Using novel approaches to evaluate behavioral interventions: overlooked significant HIV prevention effects in the HPTN 015 Project EXPLORE. Journal of Acquired Immune Deficiency Syndromes, 87(5), 1128-1135. https://doi.org/10.1097/QAI.0000000000002711
• MacKinnon, D. P., & *Lamp, S. J. (2021). A unification of mediator, confounder, and collider effects. Prevention Science, 22(8), 1185-1193. https://doi.org/10.1007/s11121-021-01268-x
• Rijnhart, J. M., *Valente, M. J., *Smyth, H., & MacKinnon, D. P. (2021). Statistical mediation analysis for models with a binary mediator and binary outcome: The differences between causal and traditional mediation analysis. Prevention Science, 24(3), 408-418.

2020

• Miočević, M., Levy, R. & MacKinnon, D. P. (2020). Different roles of prior distributions in the single mediator model with latent variables. Multivariate Behavioral Research, 56(1), 20-40. https://doi.org/10.1080/00273171.2019.1709405
• Gonzalez, O., MacKinnon, D. P., & *Muniz, F. B. (2020). Extrinsic convergent validity evidence to prevent jingle and jangle fallacies. Multivariate Behavioral Research, 56(1), 3-19. https://doi.org/10.1080/00273171.2019.1707061 Tanaka Award winner for the most outstanding paper in Multivariate Behavioral Research volume 56.
• Hsiao, Y.-Y., Tofighi, D., Kruger, E. S., Van Horn, M. L., MacKinnon, D. P., & Witkiewitz, K. (2020). The (lack of) replication of self-reported mindfulness as a mechanism of change in mindfulness-based relapse prevention for substance use disorders. Mindfulness, 10(4), 724-736.
• Gonzalez, O., & MacKinnon, D. P. (2020). The measurement of the mediator and its influence on statistical mediation conclusions. Psychological Methods, 26(1), 1-17. https://doi.org/10.1037/met0000263
• Mazza, G. L., *Smyth, H. L., Bissett, P. G., *Canning, J. R., Eisenberg, I. W., Enkavi, A. Z., *Gonzalez, O., Kim, S. J., Metcalf, S. A., *Muniz, F., Onken, L., *Pelham III, W. E., Scherer, E. A., Stoeckel, L. E., *Valente, M. J., Xie, H., Poldrack, R. A., Marsch, L. A., & Mackinnon, D. P. (2020). Correlation database of 60 cross-disciplinary surveys and cognitive tasks assessing self-regulation. Journal of Personality Assessment, 103(2), 238-245.
• Hsiao, Y., Kruger, E. S., Van Horn, M. L., Tofighi, D., MacKinnon, D. P., Witkiewitz, K. (2020). Latent class mediation: A comparison of six approaches. Multivariate Behavioral Research, 56(4), 543-557.
• Smyth, H. L., & MacKinnon, D. P. (2020). Statistical evaluation of person-oriented mediation using configural frequency analysis. Integrative Psychological and Behavioral Science, 55(3), 593-636. https://doi.org/10.1007/s12124-020-09519-2
• Valente, M. J., Rijnhart, J. J. M, *Smyth, H., *Muniz, F. B., & MacKinnon, D. P. (2020). Causal mediation programs in R, Mplus, SAS, SPSS, and STATA. Structural Equation Modeling: A Multidisciplinary Journal, 27(6), 975-984. https://doi.org/10.1080/10705511.2020.1777133
• Kisbu-Sakarya, Y., MacKinnon, D. P., & *Valente, M. J., Çetinkaya, E. (2020). Causal mediation analysis in the presence of post-treatment confounding variables: A Monte Carlo simulation study. Frontiers in Psychology, 11, Article 2067. https://doi.org/10.3389/fpsyg.2020.02067
• Rijnhart, J. M., *Valente, M. J., & MacKinnon, D. P., Twisk, J. W. R., & Heymans, M. W. (2020). The use of traditional and causal estimators for mediation models with a binary outcome and exposure mediator interaction. Structural Equation Modeling, 28(3), 345-355. https://doi.org/10.1080/10705511.2020.1811709
• Cano, M. A, Schwartz, S. J., MacKinnon, D. P., ... & de Dios, M. A (2020). Exposure to ethnic discrimination in social media and symptoms of anxiety and depression among Hispanic emerging adults: Examining the moderating role of gender. Journal of Clinical Psychology, 77(3), 571-586. https://doi.org/10.1002/jclp.23050

Mediation Analysis

Introduction to Statistical Mediation Analysis, David Mackinnon 

2024

• MacKinnon, D. P. (2024, February 14, virtual). The single mediator counterfactual model: Motivation and links with traditional mediation analysis. Invited colloquia for the University of Minnesota Psychology Department
• Virtual Workshop for the Society for Prevention Research (Planned for Summer of 2024). David P. MacKinnon, Diana Alvarez-Bartolo, Sophia J. Lamp, and Alexis Georgeson. Modern Mediation Analysis: Methods for Addressing and Testing Model Assumptions and Guidelines for Reporting Results.
• MacKinnon, D. P. (Conference July 16-19, 2024). The investigation of mediating processes as a measurement challenge. Keynote address at the International Meeting of the Psychometric Society, Prague, Czechoslovakia. 
• MacKinnon, D. P. (September 24, 2024). Invited presentation at Mediation Research Days Conference. McGill University, Montreal Canada. September 23-26. 

2/19/20 and 2/20/2020

David MacKinnon and Matthew Valente presented two workshops at the University of Miami Department of Public Health Sciences and the Center for HIV and Research on Mental Health (CHARM).

06/18/2020

David MacKinnon will present his recent mediation analysis research at a “Mind the Gap” webinar sponsored by National Institute on Health Office of Disease Prevention. 

05/30/2017

David MacKinnon, Oscar Gonzalez, Gina Mazza, Holly O'Rourke, and Matt Valente will present a one-day preconference workshop on Tuesday, May 30th before the Society for Prevention Research Conference in Washington, DC. The workshop will cover modern methods for statistical mediation.

05/01/2015

Oscar Gonzalez has been awarded the National Science Foundation Graduate Research Fellowship. The importance of this fellowship can be summarized below: 

 "The purpose of the NSF Graduate Research Fellowship Program (GRFP) is to help ensure the vitality and diversity of the scientific and engineering workforce of the United States. The program recognizes and supports outstanding graduate students who are pursuing research-based master's and doctoral degrees in science and engineering. The GRFP provides three years of full stipend support for the graduate education of individuals who have demonstrated their potential for significant achievements in science and engineering."  (Synopsis of the program -- nsf.gov)

Matt Valente has been awarded a National Research Service Award for work applying the potential outcomes model to longitudinal data.